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Mol Ecol Resour. 2017 Sep;17(5):1072-1089. doi: 10.1111/1755-0998.12629. Epub 2016 Nov 28.

High performance computation of landscape genomic models including local indicators of spatial association.

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Laboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Switzerland.
School of Biosciences, Cardiff University, Sir Martin Evans Building, Cardiff, CF10 3AX, UK.
Nicholas School of the Environment, University Program in Ecology, Duke University, Durham, NC, 27708, USA.
BioDNA - Centro di Ricerca sulla Biodiversità e sul DNA Antico, Istituto di Zootecnica, Università Cattolica del S. Cuore, via E. Parmense 84, 29100, Piacenza, Italy.
Department of Zoology, Entomology and Fisheries Sciences, College of Natural Sciences, Makerere University, Box 7062, Kampala, Uganda.
Associazione Italiana Allevatori, 00161, Roma, Italy.
Division of Biological Sciences, University of Montana, Missoula, MT, 59812, USA.
Laboratoire d'Ecologie Alpine (LECA), CNRS, Grenoble, 38000, France.
Laboratoire d'Ecologie Alpine (LECA), Univ. Grenoble Alpes, Grenoble, 38000, France.


With the increasing availability of both molecular and topo-climatic data, the main challenges facing landscape genomics - that is the combination of landscape ecology with population genomics - include processing large numbers of models and distinguishing between selection and demographic processes (e.g. population structure). Several methods address the latter, either by estimating a null model of population history or by simultaneously inferring environmental and demographic effects. Here we present samβada, an approach designed to study signatures of local adaptation, with special emphasis on high performance computing of large-scale genetic and environmental data sets. samβada identifies candidate loci using genotype-environment associations while also incorporating multivariate analyses to assess the effect of many environmental predictor variables. This enables the inclusion of explanatory variables representing population structure into the models to lower the occurrences of spurious genotype-environment associations. In addition, samβada calculates local indicators of spatial association for candidate loci to provide information on whether similar genotypes tend to cluster in space, which constitutes a useful indication of the possible kinship between individuals. To test the usefulness of this approach, we carried out a simulation study and analysed a data set from Ugandan cattle to detect signatures of local adaptation with samβada, bayenv, lfmm and an FST outlier method (FDIST approach in arlequin) and compare their results. samβada - an open source software for Windows, Linux and Mac OS X available at - outperforms other approaches and better suits whole-genome sequence data processing.


environmental correlations; genome scans; high performance computing; landscape genomics; local adaptation; spatial autocorrelation

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